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Maximizing Output to Optimizing Operations: The Evolution of AI4M in Manufacturing

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In the 1967 movie “The Graduate”, Benjamin Braddock (Dustin Hoffman) is given “one word” of advice for his future: “PLASTICS”.

Now … 56 years later … if you are in manufacturing, I am giving you TWO words of advice for your future: “BUSINESS INTELLIGENCE”

Business Intelligence ensures that a company has access to all the relevant information which its management needs to serve its market(s) as well as possible. It identifies all the elements of a company’s entire operations which need to be working cohesively to operate at its best. To be truly effective, all of a company’s operations need to work together:  They all need to be optimized into a unified solution!

Robert Ziner is the founder and CEO of Advanced Bio-Material Technologies Corporation in Toronto, Canada: He believes that it is Business Intelligence that defines the need for AI4M; and a successful AI4M solution provides immediate, direct access to all of a company’s business intelligence, which is a key to continuous improvement. The market for AI4M in 2022 was $2.3B and is projected to grow to $16.3B by 2027, representing a CAGR of 47.9%over these 5 years: a 709% increase.

Historically, the driver of automation has been to focus on maximizing output, which has resulted in a narrow focus on producing more products. However, the emergence of, Artificial Intelligence for Manufacturing (“AI4M”), has shifted the emphasis towards the process of data optimization.

 Rather than solely focusing on output, AI4M seeks to optimize the overall business operations and economics of manufacturing. By leveraging AI technologies, businesses can make more informed decisions, increase efficiency, and improve the quality of their products. Therefore, AI4M represents a paradigm shift from the traditional focus on maximizing output to a more holistic approach that takes into account the broader goals of the organization.

 To best understand the future of Industry 4.0 and the adoption of AI4M, I believe it’s important to understand the pathway that technology has taken to make this shift. 

 Automation in Manufacturing: Maximization

Manufacturing operates in a very competitive business environment. Its basic focus is: How to produce more products – for less money! It is important to note the similarity between all the iterations of “automation” between 8000 BC and t943 (see diagram below) is the principle of Maximization; Manufacturing was traditionally focused on the idea that automation enables businesses to maximize productivity and revenues.

 THE HISTORY OF AI4M IN MANUFACTURING: Optimization

 “Flexible” Automation surfaced in the early 1980s, when GE’s leading-edge Factory Automation Group developed and built the world’s first fully “computer-driven” factory to assemble locomotives in upstate New York. In this application the “Islands of Automation” from Level 2 were ultimately integrated digitally into one “linked” production enterprise which was controlled by the facility’s central computer system. This was the world’s first Computer Integrated Manufacturing (“CIM”) operation - the precursor to AI4M.

 CIM redefined the economics of production and enabled GE’s locomotive plant to maintain its status as the world’s most cost-effective - and profitable - locomotive production facility until GE sold that division in 2018.

 The opportunity wasn’t only to output more product – but also to generate better products, more dependably and cost effectively. CIM was essentially today’s IOTT without the benefit of the internet: The entire data network was limited to the limited data and processing power available on a company’s own computer system. However, it was CIM that introduced optimization into the world of manufacturing.

 Finally, in the first decade of the 21st century AI for manufacturing emerged, offering access to data via the internet, along with better operating controls with faster & more flexible process capabilities to enable more potent optimization capabilities.

 The aim since then has been to utilize real-time data to address any combination of the anticipated situations, not only in the manufacturing environment but rather, throughout the business enterprise in the most practical & profitable way possible: Predictive solutions utilize a computer-generated response to achieve optimization by fulfilling as many of the “anticipated”  situations as determined by the predictive capabilities inherent in AI4M .

AI for manufacturing analyzes both real-time as well as historical data to achieve fully optimized solutions. Because data is knowledge, AI4M utilizes this inherent ability to redefine the overall economics of production which in turn redefines almost all business intelligence1

In today’s ultra-fast moving world, it is clear that Business Intelligence – not throughout  -is the key to redefining processing and production opportunities.

 To learn more about the automation and AI for manufacturing, checkout our  webinar with guest Robert Ziner and Nirav Raiyani: https://ai4manufacturing.ca/blog/how-ai-in-manufacturing-is-different-than-automation-2023-03-23

 

#AI #Ai4Manufacturing #manufacturing #AI4M #innovation #history #collaboration #automation 

 

 

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